Automatic analysis of double coronal mass ejections from coronagraph images. Issue 11 (17th November 2015)

• Record Type: Journal Article
• Authors: Jacobs, Matthew; Chang, Lin‐Ching; Pulkkinen, Antti; Romano, Michelangelo
• Is Part Of: Space weather. Volume 13:Issue 11(2015:Nov.)
• Page Start: 761
• Page End: 777
• Publication Date: 2015-11-17
• Subjects: coronagraph images -- automatic CME processing and analysis -- double‐CME classification; Space environment -- Periodicals; 551.509992
• DOI: 10.1002/2015SW001260
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Coronal mass ejections (CMEs) can have major impacts on man‐made technology and humans, both in space and on Earth. These impacts have created a high interest in the study of CMEs in an effort to detect and track events and forecast the CME arrival time to provide time for proper mitigation. A robust automatic real‐time CME processing pipeline is greatly desired to avoid laborious and subjective manual processing. Automatic methods have been proposed to segment CMEs from coronagraph images and estimate CME parameters such as their heliocentric location and velocity. However, existing methods suffered from several shortcomings such as the use of hard thresholding and an inability to handle two or more CMEs occurring within the same coronagraph image. Double‐CME analysis is a necessity for forecasting the many CME events that occur within short time frames. Robust forecasts for all CME events are required to fully understand space weather impacts. This paper presents a new method to segment CME masses and pattern recognition approaches to differentiate two CMEs in a single coronagraph image. The proposed method is validated on a data set of 30 halo CMEs, with results showing comparable ability in transient arrival time prediction accuracy and the new ability to automatically predict the arrival time of a double‐CME event. The proposed method is the first automatic method to successfully calculate CME parameters from double‐CME events, making this automatic method applicable to a wider range of CME events. Key Points: An automatic CME processing pipeline using coronagraph images The proposed method can differentiate multiple CMEs occurring in one coronagraph image Validation was performed using estimated CME measurements and predicted CME arrival times at L1